Project name: Complexity in Economic Learning Processes - Modeling of a Complex Learning Environment in the Field of Stock Market Games and Development of a Technology to Detect Phase Transitions (Learning Thresholds) in Nonlinear Dynamic Learning Scenarios in Economics.

Period: since 2009

Contact: Univ.-Prof. Dr. Andreas Liening, Dr. Ewald Mittelstädt

Project partner: Dr. Dr. Guido Strunk (WU Vienna)

Abstract: Numerous studies (e.g. SFB 823: "Nonlinear Dynamical Models in Economics and Technology", speaker university TU Dortmund University) in economics try to give an empirical basis to theoretical approaches of interpreting economic processes as non-linear, dynamic processes by means of the theories of Nonlinear Dynamical Systems. This involves research into how the irregular fluctuations and jumps in economic processes (structural breaks) observable in reality can be analyzed and how the complexity of such phenomena can be measured (Liening 1999: 200ff.).

For example, exchange rate fluctuations, stock price developments or gross national product developments and the associated time series analyses are examined. In addition to the calculation of the dimensionality of attractors, the focus is on the investigation of Lyapunov exponents, which reflect the sensitive dependence on the initial conditions of a system and can be a measure for the complexity of a system.

So far, empirical results have only partially fulfilled the expectations placed on them (Liening 2006). Due to the fact that the economy has to be considered as an open system, the dynamic behaviors are overlaid by exogenous shocks and stochastic influences (Liening 1999: 208f.). Eliminating this noise in the data has hardly been possible so far. In the meantime, there are approaches that rely on a combination of diverse methods that allow complex systems to be examined profitably: While the traditionally often used information theory has often proven to be an unsuitable approach due to both exponential divergence as a core feature of deterministic chaos and non-periodicity, which is more strictly defined in nonlinear dynamical systems, an algorithmic definition of complexity based on Grammar Complexity is able to identify patterns of order in chaos. In this context, the correlation dimension and the recurrence plots, respectively, allow to capture complexity of systems via their dynamics and to quantify the order of a dynamic via the temporal duration of the matches found. The entropy concept can also be seen as helpful, which defines complexity in the form of permutation entropy in terms of the frequency distributions of sequence patterns of economic processes (Strunk 2009: 205ff.).

The shortcoming that the algorithms described in the literature are often not available as usable software was recently solved in a dissertation. This software is available to the project as preliminary work by my doctoral student Guido Strunk (Strunk 2009: 299ff.).

In the context of the research project it is planned to investigate the learning processes of actors in nonlinear dynamic systems and to investigate by appropriate empirical methods of the theories of Nonlinear Dynamical Systems whether or under which boundary conditions there are homomorphisms between the nonlinear dynamic economic processes and the learning processes of the actors. In particular, phase transitions as learning thresholds in structural breaks are focused on, as they are discussed in the context of synergetics as one of numerous theories of Nonlinear Dynamical Systems.

With the help of an experimental business game on the financial market, a non-linear dynamic system is simulated and the economic learning processes of the actors are surveyed multivariately. Time series analyses of the recorded variables are used to examine their degree of complexity, whether phase transitions are detectable and in what way these are related to structural breaks in the economic processes. The experimental business game is accompanied by different learning scenarios so that the contribution of different (business) didactic approaches such as the classical business game methodology, learning diaries or threshold concepts to the actors' acquisition of competencies can be measured. In particular, the threshold concepts currently discussed in international business didactics research are closely related to phase transitions. In this way, an empirical indication of this approach can verify the learning effectiveness assumed so far.

Liening, Andreas (1999): Complex Systems between Order and Chaos. Recent developments in the theory of non-linear systems and their relevance for economics and its didactics. Hamburg, London, Münster.

Liening, Andreas (2006): iLearning - Selbstorganisiertes Lernen im Rahmen ökonomischer Bildung. In: Timo Meynhardt and Ewald J. Brunner (Eds.): Managing Self-Organization. Contributions to the synergetics of organization. Münster.

Liening, Andreas (2009): Complexonomics - Über den Zusammenbruch des Laplaceschen Weltbildes, den Einzug der Komplexität in die Wirtschaftswissenschaft und die Anmaßung von Wissen. In: Johannes Weyer and Ingo Schulz-Schaeffer (eds.): Management of Complex Systems. Munich. Strunk, G. (2009) The complexity hypothesis in career research. Frankfurt.